This study seeks to identify if the urban or rural classification in the 2019 Ghana Malaria Indicator Survey corresponds with the presence of constructed buildings captured from satellite data. This information will guide further research combining geospatial data and disease prevalence data to determine the association between living in urban areas and the health of individuals.
The map was created using the tmap package (version 3.3) in R statistical software 4.0.3.
Vector data from the 2019 Ghana Malaria Indicator Survey from the DHS program website (www.dhsprogram.com) was overlaid with raster data of built up areas from the WorldPopProject hosted by the University of Southampton and Global Human settlement data from the European Commission Repository.
GPS location data was collected from the survey enumeration areas from the 2019 Ghana Malaria Indicator Survey (GMIS) data downloaded with approval from the Demographic and Health Survey program website https://dhsprogram.com/methodology/survey/survey-display-557.cfm. This dataset has a column that designates a geospatial cluster as urban or rural.
The density of built up areas in Ghana as at February 2021 for a 100m grid cell resolution was downloaded from www.worldpop.org. The density of buildings in the area was calculated by dividing the count of buildings divided by the area. This was compared to raster data from the 2014 Global Human settlement data which had a 250m grid cell resolution.
Accra and Kumasi, Ghana’s largest cities have a cluster of yellow circles (urban areas according to the Ghana Malaria Indicator Survey) from Map 1, deeper orange shaded areas from the 2021 WorldPop Ghana built up raster data from Map 2 and black shaded areas from the 2014 Ghana GHSL raster data in Map3 showing that there are a lot built up areas that is constructed buildings in Accra (south of Ghana) and Kumasi (middle belt of Ghana).
Since there is a high correlation between the areas with yellow circles (classified as urban areas according to GMIS data), the deeper orange shaded areas (classified as urban areas according to 2021 WorldPop data) and the black shaded areas in Map3 (classified as urban areas according to the 2014 Ghana GHSL built data), raster data obtained from satellites can be classified as a good measure of classifying urban areas.
Thus, in subsequent studies, satellite raster data can be combined with disease prevalence data to investigate the effect of living in urban areas has on a person’s health.